Abstract

A number of techniques for handling missing data have been presented and implemented. Most of these proposed techniques are unnecessarily complex and, therefore, difficult to use. This chapter investigates a hot-deck data imputation method, based on rough set computations. In this chapter, characteristic relations are introduced that describe incompletely specified decision tables and then these are used for missing data estimation. It has been shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable insight into the problem of missing data. Missing data are predicted with an accuracy of up to 99%.